The Human Annotation Tool is a tool that allows one to annotate people - where their arms and legs are, what their 3D pose is, which body parts are occluded, etc. A database of annotated people would be invaluable for creating computer vision algorithms to detect and localize people.
You may run a copy of the tool by clicking on the above image and agreeing to all the disclaimers. You need to have Java and a reasonably good graphics card. The tool supports two kinds of annotations - labeling joints and extracting the 3D pose, and labeling the regions of the body hair, face, upper clothes, etc. To jump to a given annotation, put its index in the " Current Entry " box. Use the arrows to go to an image containing a person that is not annotated. Pan and zoom to the person.
Move the mouse over the location of each keypoint and press the corresponding key, indicated with a picture of the body part. You may pick and drag keypoints to adjust their locations.
The keys can be changed from the configuration file. If a keypoint is occluded or falls outside the image but you have a rough guess where it should be, mark it as best as you can.
Leave keypoints unmarked if you have no idea where they should lie. Both shoulders. The joint location in 3D is the intersection of the axes of adjoining cylinders.
Left vs Right : The keypoint is labelled as left or right from the point of view of the labelled person, not based its location in the image.
For example, if the person is facing the camera his or her left keypoints lie on the right in image space. Nose Tip : The location is the tip of the nose, regardless of frontal or profile view. Eyes : In frontal view it is the midpoint of the two eye corners.
The eye location does not depend on the pupils. In profile view it is the tip of the eye surface. Even if the eye is closed, we estimate the tip of the eye surface, ignoring the eyelids. Ears : The tip of the tragus the small pointed eminence of the external ear. When you hover with the mouse over a keypoint, use the red keys to specify keypoint properties or to delete it. Press N to mark a keypoint as occluded.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This is the toolbox for the ObjectNet3D database introduced for 3D object recognition.
ObjectNet3D consists of categories, 90, images,objects in these images. Objects in the images in our database are aligned with the 3D shapes, and the alignment provides both accurate 3D pose annotation and the closest 3D shape annotation for each 2D object. Check the code of this function to understand the annotation format of ObjectNet3D.
Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit aaf07a8 Feb 2, You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Feb 2, Sep 2, GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The paper is available here. Video generation with a single image as input. More details can be found in the supplementary materials in our paper. In pytorch 1. Only for the DeepFashion dataset. This is Pytorch implementation for pose transfer on both Market and DeepFashion dataset.
The code is written by Tengteng Huang and Zhen Zhu. We provide our dataset split files and extracted keypoints files for convience. Note: In our settings, we crop the images of DeepFashion into the resolution of x in a center-crop manner.
We use OpenPose to generate keypoints. For evaluation, Tensorflow 1. Our pre-trained model can be downloaded Google Drive or Baidu Disk. Our code is based on the popular pytorch-CycleGAN-and-pix2pix. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. Python Shell.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.
If nothing happens, download the GitHub extension for Visual Studio and try again. Besides, it also supports YOLO format. However, Python 3 or above and PyQt5 are strongly recommended.
Note: The Last command gives you a nice. Install PythonPyQt5 and install lxml. You can pull the image which has all of the installed and required dependencies. Watch a demo video.
A txt file of YOLO format will be saved in the same folder as your image with same name. A file named "classes. When pressing space, the user can flag the image as verified, a green background will appear. This is used when creating a dataset automatically, the user can then through all the pictures and flag them instead of annotate them. The difficult field is set to 1 indicates that the object has been annotated as "difficult", for example, an object which is clearly visible but difficult to recognize without substantial use of context.
According to your deep neural network implementation, you can include or exclude difficult objects during training. Free software: MIT license. Citation: Tzutalin. Git code Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up.See the challenge description and description of the new evaluation metric and the evaluation server.
Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. ArXiv GitHub. We involve human annotators to establish dense correspondences from 2D images to surface-based representations of the human body.
If done naively, this would require by manipulating a surface through rotations - which can be frustratingly inefficient.
Amazon Mechanical Turk for LabelMe
Instead, we construct a two-stage annotation pipeline to efficiently gather annotations for image-to-surface correspondence. As shown below, in the first stage we ask annotators to delineate regions corresponding to visible, semantically defined body parts. We instruct the annotators to estimate the body part behind the clothes, so that for instance wearing a large skirt would not complicate the subsequent annotation of correspondences.
In the second stage we sample every part region with a set of roughly equidistant points and request the annotators to bring these points in correspondence with the surface. This allows the annotator to choose the most convenient point of view by selecting one among six options instead of manually rotating the surface.
Poselets and Their Applications in High-Level Computer Vision
The two-stage annotation process has allowed us to very efficiently gather highly accurate correspondences. We have seen that the part segmentation and correspondence annotation tasks take ap- proximately the same time, which is surprising given the more challenging nature of the latter task. We have gathered annotations for 50K humanscollecting more then 5 million manually annotated correspondences. Below are visualizations of annotations on images from our validation set: Image leftU middle and V right values for the collected points.
Similar to DenseRegour strategy to find dense correspondence by partitioning the surface. And for every pixel, determine: which surface part it belons to, where on the 2D paremeterization of the part it corresponds to.
On the right, partitioning of the surface and "correspondence to a point on a part" is demonstrated. As shown below, we introduce a fully-convolutional network on top of the ROI-pooling that is entirely devoted to two tasks: Generating per-pixel classification results for selection of surface part. For each part regressing local coordinates within part.
During inference, our system operates at 25fps on x images and fps on x images using a GTX graphics card. We further improve the performance of our system using cascading strategies.
Via cascadning, we exploit information from related tasks, such as keypoint estimation and instance segmentation, which have successfully been addressed by the Mask-RCNN architecture. This allows us to exploit task synergies and the complementary merits of different sources of supervision. Iasonas Kokkinos University College London.Want to outsource your labeling task to the internet? Amazon Mechanical Turk allows access to many internet users who are ready to perform tasks for a fixed price.
The idea is simple: you provide a task and a selling price. Internet workers perform the task and are subsequently paid. In Mechnical Turk terminology, tasks are called "HITs", people requesting work are called "Requesters", and people who do the work are called "Workers". This page describes how to set up LabelMe annotation tasks onto Mechanical Turk. The process is simple, as we have provided scripts for creating and sending LabelMe annotation tasks onto Mechanical Turk.
ggtree: visualization and annotation of phylogenetic trees
All you have to do is follow the instructions below and pay workers on Mechanical Turk to label images. We collect the annotations, which are immediately available for download. In this way, everybody wins: Mechanical Turk workers get paid, you get your images annotated, and the computer vision community gets access to more hand-labeled data.
Setting up LabelMe on Mechanical Turk is easy. The following are instructions for setting up LabelMe on Mechanical Turk. You will need to set up an account as a Requester on Mechanical Turk. Instructions for setting up an account are here. Once you have created an account, sign in and try to access your accountalong with the sandbox used for debugging. The tools provide the backbone for communicating with the Mechanical Turk servers. To start, you first need to request your access key and secret key.
This is different than your username and password. To do this, create an Amazon Web Services account. Here, you will find your access key and secret key. Unzip the file and follow the instructions inside the directory to install the Command Line Tools. As a reminder, open and modify. Also, make sure you set the following environment variables e. We provide a set of scripts that are used to interact with Mechanical Turk and set how the task is performed e.
We maintain the latest version of the code on github. You can refresh your copy to the latest version by running "git pull" from inside the project directory. If you have an idea for a new feature and want to implement it, then let us know!We present two novel solutions for multi-view 3D human pose estimation based on new learnable triangulation methods that combine 3D information from multiple 2D views.
The first baseline solution is a basic differentiable algebraic triangulation with an addition of confidence weights estimated from the input images. The secondmore complex, solution is based on volumetric aggregation of 2D feature maps from the 2D backbone followed by refinement via 3D convolutions that produce final 3D joint heatmaps.
Crucially, both of the approaches are end-to-end differentiable, which allows us to directly optimize the target metric. We demonstrate transferability of the solutions across datasets and considerably improve the multi-view state of the art on the Human3.
Note : Here and further we report only summary of our results. Please refer to our paper [cite] for more details. MPJPE absolute filtered scenes with non-valid ground-truth annotations :. We demonstrate that the learnt model is able to transfer between different coloring and camera setups without any finetuning see video demonstration. Our approaches assume we have synchronized video streams from cameras with known projection matrices capturing performance of a single person in the scene.
We aim at estimating the global 3D positions of a fixed set of human joints with indices. Note : Here we present only short overview of our methods. Our first approach is based on algebraic triangulation with learned confidences. The 2D positions of the joints are inferred from 2D joint heatmaps by applying soft-argmax with inverse temperature parameter :. The 2D positions together with the confidences are passed to the algebraic triangulation module which solves triangulation problem in the form of system of weighted linear equations:.
All blocks allow backpropagation of the gradients, so the model can be trained end-to-end. Then feature maps are unprojected into a volume with a per-view aggregation see animation below :.
Operation denotes bilinear sampling. The volume is passed to a 3D convolutional neural network that outputs the interpretable 3D heatmaps. The output 3D positions of the joints are inferred from 3D joint heatmaps by computing soft-argmax :. Unlike the algebraic methodvolumetric has 3D convolutional neural network, which is able to model human pose prior. Volumetric model is also fully differentiable and can be trained end-to-end.
There are some 3D pose annotation errors in the Human3.